{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pregnants</th>\n",
       "      <th>Plasma_glucose_concentration</th>\n",
       "      <th>blood_pressure</th>\n",
       "      <th>Triceps_skin_fold_thickness</th>\n",
       "      <th>serum_insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>Diabetes_pedigree_function</th>\n",
       "      <th>Age</th>\n",
       "      <th>Target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6</td>\n",
       "      <td>148</td>\n",
       "      <td>72</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>33.6</td>\n",
       "      <td>0.627</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>85</td>\n",
       "      <td>66</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>26.6</td>\n",
       "      <td>0.351</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8</td>\n",
       "      <td>183</td>\n",
       "      <td>64</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>23.3</td>\n",
       "      <td>0.672</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>89</td>\n",
       "      <td>66</td>\n",
       "      <td>23</td>\n",
       "      <td>94</td>\n",
       "      <td>28.1</td>\n",
       "      <td>0.167</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>137</td>\n",
       "      <td>40</td>\n",
       "      <td>35</td>\n",
       "      <td>168</td>\n",
       "      <td>43.1</td>\n",
       "      <td>2.288</td>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   pregnants  Plasma_glucose_concentration  blood_pressure  \\\n",
       "0          6                           148              72   \n",
       "1          1                            85              66   \n",
       "2          8                           183              64   \n",
       "3          1                            89              66   \n",
       "4          0                           137              40   \n",
       "\n",
       "   Triceps_skin_fold_thickness  serum_insulin   BMI  \\\n",
       "0                           35              0  33.6   \n",
       "1                           29              0  26.6   \n",
       "2                            0              0  23.3   \n",
       "3                           23             94  28.1   \n",
       "4                           35            168  43.1   \n",
       "\n",
       "   Diabetes_pedigree_function  Age  Target  \n",
       "0                       0.627   50       1  \n",
       "1                       0.351   31       0  \n",
       "2                       0.672   32       1  \n",
       "3                       0.167   21       0  \n",
       "4                       2.288   33       1  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.read_csv('pima-indians-diabetes.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 768 entries, 0 to 767\n",
      "Data columns (total 9 columns):\n",
      "pregnants                       768 non-null int64\n",
      "Plasma_glucose_concentration    768 non-null int64\n",
      "blood_pressure                  768 non-null int64\n",
      "Triceps_skin_fold_thickness     768 non-null int64\n",
      "serum_insulin                   768 non-null int64\n",
      "BMI                             768 non-null float64\n",
      "Diabetes_pedigree_function      768 non-null float64\n",
      "Age                             768 non-null int64\n",
      "Target                          768 non-null int64\n",
      "dtypes: float64(2), int64(7)\n",
      "memory usage: 54.1 KB\n"
     ]
    }
   ],
   "source": [
    "df.info()\n",
    "#数据量比较小，数据没有缺失,serum_insulin,0值比较多"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fa5058b4650>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(df.serum_insulin)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 936x648 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#serum_insulin 大部分是0，是长尾分布，考虑log(x+1) 变换,减弱长尾中大特征值的影响 \n",
    "cols=df.columns\n",
    "feat_corr=df.corr().abs()\n",
    "plt.subplots(figsize=(13,9))\n",
    "sns.heatmap(feat_corr,annot=True,)\n",
    "plt.show()\n",
    "#发现没有很大对关联"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pregnants_log</th>\n",
       "      <th>Plasma_glucose_concentration_log</th>\n",
       "      <th>blood_pressure_log</th>\n",
       "      <th>Triceps_skin_fold_thickness_log</th>\n",
       "      <th>serum_insulin_log</th>\n",
       "      <th>BMI_log</th>\n",
       "      <th>Diabetes_pedigree_function_log</th>\n",
       "      <th>Age_log</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.945910</td>\n",
       "      <td>5.003946</td>\n",
       "      <td>4.290459</td>\n",
       "      <td>3.583519</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.543854</td>\n",
       "      <td>0.486738</td>\n",
       "      <td>3.931826</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.693147</td>\n",
       "      <td>4.454347</td>\n",
       "      <td>4.204693</td>\n",
       "      <td>3.401197</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.317816</td>\n",
       "      <td>0.300845</td>\n",
       "      <td>3.465736</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.197225</td>\n",
       "      <td>5.214936</td>\n",
       "      <td>4.174387</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.190476</td>\n",
       "      <td>0.514021</td>\n",
       "      <td>3.496508</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.693147</td>\n",
       "      <td>4.499810</td>\n",
       "      <td>4.204693</td>\n",
       "      <td>3.178054</td>\n",
       "      <td>4.553877</td>\n",
       "      <td>3.370738</td>\n",
       "      <td>0.154436</td>\n",
       "      <td>3.091042</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.927254</td>\n",
       "      <td>3.713572</td>\n",
       "      <td>3.583519</td>\n",
       "      <td>5.129899</td>\n",
       "      <td>3.786460</td>\n",
       "      <td>1.190279</td>\n",
       "      <td>3.526361</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   pregnants_log  Plasma_glucose_concentration_log  blood_pressure_log  \\\n",
       "0       1.945910                          5.003946            4.290459   \n",
       "1       0.693147                          4.454347            4.204693   \n",
       "2       2.197225                          5.214936            4.174387   \n",
       "3       0.693147                          4.499810            4.204693   \n",
       "4       0.000000                          4.927254            3.713572   \n",
       "\n",
       "   Triceps_skin_fold_thickness_log  serum_insulin_log   BMI_log  \\\n",
       "0                         3.583519           0.000000  3.543854   \n",
       "1                         3.401197           0.000000  3.317816   \n",
       "2                         0.000000           0.000000  3.190476   \n",
       "3                         3.178054           4.553877  3.370738   \n",
       "4                         3.583519           5.129899  3.786460   \n",
       "\n",
       "   Diabetes_pedigree_function_log   Age_log  \n",
       "0                        0.486738  3.931826  \n",
       "1                        0.300845  3.465736  \n",
       "2                        0.514021  3.496508  \n",
       "3                        0.154436  3.091042  \n",
       "4                        1.190279  3.526361  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y=df['Target']\n",
    "_X=df.drop('Target',axis=1)\n",
    "_X=np.log1p(_X)\n",
    "cate=_X.columns+'_log'\n",
    "X=pd.DataFrame(columns=cate,data=_X.values)\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "#对数据进行缩放，考虑0值多，采用minmaxscaler\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "ms_log=MinMaxScaler()\n",
    "feat_names_log=X.columns\n",
    "X_train_log = ms_log.fit_transform(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pregnants_log</th>\n",
       "      <th>Plasma_glucose_concentration_log</th>\n",
       "      <th>blood_pressure_log</th>\n",
       "      <th>Triceps_skin_fold_thickness_log</th>\n",
       "      <th>serum_insulin_log</th>\n",
       "      <th>BMI_log</th>\n",
       "      <th>Diabetes_pedigree_function_log</th>\n",
       "      <th>Age_log</th>\n",
       "      <th>Target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.673239</td>\n",
       "      <td>0.944441</td>\n",
       "      <td>0.891583</td>\n",
       "      <td>0.778151</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.839581</td>\n",
       "      <td>0.356534</td>\n",
       "      <td>0.639050</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.239812</td>\n",
       "      <td>0.840710</td>\n",
       "      <td>0.873760</td>\n",
       "      <td>0.738561</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.786030</td>\n",
       "      <td>0.195523</td>\n",
       "      <td>0.284791</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.760188</td>\n",
       "      <td>0.984263</td>\n",
       "      <td>0.867462</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.755862</td>\n",
       "      <td>0.380165</td>\n",
       "      <td>0.308180</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.239812</td>\n",
       "      <td>0.849290</td>\n",
       "      <td>0.873760</td>\n",
       "      <td>0.690106</td>\n",
       "      <td>0.675479</td>\n",
       "      <td>0.798568</td>\n",
       "      <td>0.068711</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.929966</td>\n",
       "      <td>0.771702</td>\n",
       "      <td>0.778151</td>\n",
       "      <td>0.760921</td>\n",
       "      <td>0.897058</td>\n",
       "      <td>0.965907</td>\n",
       "      <td>0.330870</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   pregnants_log  Plasma_glucose_concentration_log  blood_pressure_log  \\\n",
       "0       0.673239                          0.944441            0.891583   \n",
       "1       0.239812                          0.840710            0.873760   \n",
       "2       0.760188                          0.984263            0.867462   \n",
       "3       0.239812                          0.849290            0.873760   \n",
       "4       0.000000                          0.929966            0.771702   \n",
       "\n",
       "   Triceps_skin_fold_thickness_log  serum_insulin_log   BMI_log  \\\n",
       "0                         0.778151           0.000000  0.839581   \n",
       "1                         0.738561           0.000000  0.786030   \n",
       "2                         0.000000           0.000000  0.755862   \n",
       "3                         0.690106           0.675479  0.798568   \n",
       "4                         0.778151           0.760921  0.897058   \n",
       "\n",
       "   Diabetes_pedigree_function_log   Age_log  Target  \n",
       "0                        0.356534  0.639050       1  \n",
       "1                        0.195523  0.284791       0  \n",
       "2                        0.380165  0.308180       1  \n",
       "3                        0.068711  0.000000       0  \n",
       "4                        0.965907  0.330870       1  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = pd.Series(data = y, name = 'Target')\n",
    "train_log = pd.concat([pd.DataFrame(columns = feat_names_log, data = X_train_log), y], axis = 1)\n",
    "train_log.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "logloss of each fold is:  [-0.68181818 -0.69480519 -0.66233766 -0.67973856 -0.64052288]\n",
      "cv logloss is: -0.6718444953739071\n"
     ]
    }
   ],
   "source": [
    "#Train\n",
    "y_train=train_log['Target']\n",
    "X_train=train_log.drop(['Target'],axis=1)\n",
    "feat_names = X_train.columns\n",
    "#默认参数的Logistic Regression\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "lr=LogisticRegression()\n",
    "from sklearn.model_selection import cross_val_score\n",
    "loss=cross_val_score(lr,X_train,y_train,cv=5) #5折交叉验证\n",
    "print 'logloss of each fold is: ',-loss\n",
    "print'cv logloss is:', -loss.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.5484138445868183\n",
      "{'penalty': 'l2', 'C': 100}\n"
     ]
    }
   ],
   "source": [
    "#正则化的 Logistic Regression及参数调优\n",
    "from sklearn.model_selection import StratifiedKFold\n",
    "fold = StratifiedKFold(n_splits=5, shuffle=True, random_state=777)\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "penaltys = ['l1','l2']\n",
    "Cs = [0.001, 0.01, 0.1, 1, 10, 100, 1000]\n",
    "tuned_parameters = dict(penalty = penaltys, C = Cs)\n",
    "\n",
    "lr_penalty= LogisticRegression(solver='liblinear')\n",
    "grid= GridSearchCV(lr_penalty, tuned_parameters,cv=fold, scoring='neg_log_loss',n_jobs = 4,)#用log似然损失\n",
    "grid.fit(X_train,y_train)\n",
    "print(-grid.best_score_)\n",
    "print(grid.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-0.75390625\n",
      "{'penalty': 'l1', 'C': 100}\n"
     ]
    }
   ],
   "source": [
    "\n",
    "lr_penalty= LogisticRegression(solver='liblinear')\n",
    "grid= GridSearchCV(lr_penalty, tuned_parameters,cv=fold, scoring='accuracy',n_jobs = 4,) #正确率\n",
    "grid.fit(X_train,y_train)\n",
    "print(-grid.best_score_)\n",
    "print(grid.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "import cPickle\n",
    "cPickle.dump(grid.best_estimator_, open(\"pima.pkl\", 'wb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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